Pushing computer vision from the task-specific era toward general-purpose visual intelligence — just as NLP evolved from task-specific models into general-purpose language intelligence.
GenCeption repurposes a pre-trained video generative model into a single unified, general-purpose, feed-forward vision model that solves a wide range of vision tasks with SOTA performance — all steered by text instructions, with exceptional learning efficiency and intriguing emergent behaviors.
GenCeption leverages video generation models as representation pretraining, and conducts multi-task post-training in a unified architecture.
GenCeption handles both dense and sparse vision tasks, transforming the multi-step generative backbone into a single-step feed-forward model.
GenCeption is a SOTA unified model on various vision tasks, with exceptional learning efficiency and intriguing emerging behaviors.
GenCeption is able to seamlessly switch between different vision tasks, just like how humans do in real life.
Given a natural-language expression, GenCeption accurately segments the referred object — reasoning about colors, spatial relationships and motion — and generalizes to unseen objects (e.g., a rocket) by leveraging knowledge from text-to-video pre-training.
As an example of pushing the boundaries of understanding the complex visual world, GenCeption predicts 4D human keypoints under challenging conditions (complex motion, occlusion, ego-centric, multi-view, etc.) — robust 4D human understanding that underpins real-world applications across robotics and AR/VR.
From a single video, GenCeption predicts per-pixel geometry and camera pose that lift the scene into a 4D point cloud — enabling free-viewpoint fly-throughs and language grounding of objects from a text instruction.
Although fine-tuned predominantly on synthetic human videos, GenCeption transfers seamlessly from simulation to real footage and to out-of-distribution categories — evidence of a universal "world model" inside generative video backbones.
The following acknowledgements are not exhaustive. We are also grateful to many others whose support, feedback, and discussions contributed to this work but who are not individually acknowledged here.
We are deeply grateful to Jeremiah Harmsen, Joëlle Barral, Chris Dyer, Rahul Sukthankar, Junlin Zhang, Miki Rubinstein, Thabo Beeler, Di Qiu, Jesús Pérez, Alberto García García, Sergio Orts Escolano, Erroll Wood, Iker J. de los Mozos, and Emily Conn for their invaluable support throughout this project.
We also thank the following individuals (listed alphabetically by last name) for insightful research discussions, technical feedback, and valuable conversations on implementation details: Thiemo Alldieck, Yanrui Bin, Ang Cao, Minghao Chen, Carlton Chu, Dima Damen, Shlomi Fruchter, Valentin Gabeur, Robert Geirhos, Tengda Han, Zeren Jiang, Linyi Jin, Ruofan Liang, Ziwei Liao, Haotong Lin, Xingchao Liu, Yibo Liu, Shangbang Long, Viorica Patraucean, Cordelia Schmid, Hao Shao, Jianyuan Wang, Luyu Wang, Thaddäus Wiedemer, Ziyi Wu, Yuxi Xiao, Junyu Xie, Wei Yu, Shangzhan Zhang, and Shuhong Zheng.
Also see the authors' personal thoughts after the project.
@inproceedings{wang2026genception,
title = {Video Generation Models are General-Purpose Vision Learners},
author = {Wang, Letian and Zhang, Chuhan and Kabra, Rishabh and Uijlings, Jasper and
Waslander, Steven and Zisserman, Andrew and Carreira, Joao and He, Kaiming and
Andriluka, Misha and Bazavan, Eduard Gabriel and Zanfir, Andrei and Sminchisescu, Cristian},
booktitle = {European Conference on Computer Vision (ECCV)},
year = {2026}
}
Project page for “Video Generation Models are General-Purpose Vision Learners.”